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<article id="content">
<header>
<h1 class="title">Module <code>silk.metrics.keypoint</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from typing import Iterable, Optional, Any

import torch
from torchmetrics import AveragePrecision


class KeypointDetectionAveragePrecision(AveragePrecision):
    &#34;&#34;&#34;Average Precision computed using keypoint position and probabilities.

    This metric implementation has been inspired from :

    * SuperPoint Paper (Appendix A) : https://arxiv.org/pdf/1712.07629.pdf
    * Tensorflow Implementation : https://github.com/rpautrat/SuperPoint (`superpoint/evaluations/detector_evaluation.py`).
    &#34;&#34;&#34;

    def __init__(
        self,
        distance_threshold: float = 2.0,
        allow_multimatching: bool = True,
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
    ) -&gt; None:
        &#34;&#34;&#34;

        Parameters
        ----------
        distance_threshold : float, optional
            Distance from target under which a prediction would be considered valid, by default 2.0
        allow_multimatching : bool, optional
            Allow multiple prediction to match to the same target or not, by default True
        compute_on_step : bool, optional
            Option passed to parent class (c.f. `torchmetrics.Metric`), by default True
        dist_sync_on_step : bool, optional
            Option passed to parent class (c.f. `torchmetrics.Metric`), by default False
        process_group : Optional[Any], optional
            Option passed to parent class (c.f. `torchmetrics.Metric`), by default None
        &#34;&#34;&#34;
        super().__init__(
            num_classes=1,
            compute_on_step=compute_on_step,
            dist_sync_on_step=dist_sync_on_step,
            process_group=process_group,
        )
        self._distance_threshold = distance_threshold
        self._allow_multimatching = allow_multimatching

    def update(
        self, preds: Iterable[torch.Tensor], target: Iterable[torch.Tensor]
    ) -&gt; None:
        &#34;&#34;&#34;Update the metric providing predictions and targets.

        Parameters
        ----------
        preds : Iterable[torch.Tensor]
            Iterable set of predictions. Each prediction should be a N x 3 tensor where `preds[:, 2:]` are the positions and `preds[:, 2]` are the probabilities.
        target : Iterable[torch.Tensor]
            Iterable set of targets. Each target should be a N x 2 tensor containing the targets positions.
        &#34;&#34;&#34;
        ap_target = []
        ap_preds = []

        for p, t in zip(preds, target):
            # skip if no target and no prediction
            if t.numel() == 0 and p.numel() == 0:
                continue
            elif t.numel() == 0:
                # false positives (only predictions, no targets)
                pred_prob = p[:, 2]
                ap_preds.extend(pred_prob.detach().cpu().numpy())
                ap_target.extend([0] * p.shape[0])

            elif p.numel() == 0:
                # false negatives (only targets, no preditions)
                ap_preds.extend([0.0] * t.shape[0])
                ap_target.extend([1] * t.shape[0])
            else:
                # get probability
                pred_prob = p[:, 2]

                # get positions
                pred_posi = p[:, :2].unsqueeze(1)
                targ_posi = t.unsqueeze(0)

                # sort preds in descencing order
                pred_idx = torch.argsort(pred_prob, descending=True)
                pred_prob = pred_prob[pred_idx]
                pred_posi = pred_posi[pred_idx]

                # keep track of unmatched targets
                targ_not_matched = torch.ones(t.shape[0], dtype=bool, device=t.device)

                # compute pairwise distances and potential matches
                delta = pred_posi - targ_posi
                dist = torch.norm(delta, dim=2)
                matches = dist &lt;= self._distance_threshold

                # assign predictions
                for i, m in enumerate(matches):
                    # in non-simplified version, we remove previously matched targets from potential matches
                    if not self._allow_multimatching:
                        m = torch.logical_and(m, targ_not_matched)

                    correct = torch.any(m)
                    if correct:
                        # get closest target among potential matches
                        ap_target_idx = torch.argmin(dist[i] * m)
                        ap_target.append(1)
                        targ_not_matched[ap_target_idx] = False
                    else:
                        ap_target.append(0)
                    ap_preds.append(pred_prob[i])

                # add false negative (unmatched targets)
                for _, not_matched in enumerate(targ_not_matched):
                    if not_matched:
                        ap_preds.append(0.0)
                        ap_target.append(1)

        super().update(
            torch.tensor(ap_preds, device=p.device),
            torch.tensor(ap_target, device=p.device),
        )</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.metrics.keypoint.KeypointDetectionAveragePrecision"><code class="flex name class">
<span>class <span class="ident">KeypointDetectionAveragePrecision</span></span>
<span>(</span><span>distance_threshold: float = 2.0, allow_multimatching: bool = True, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None)</span>
</code></dt>
<dd>
<div class="desc"><p>Average Precision computed using keypoint position and probabilities.</p>
<p>This metric implementation has been inspired from :</p>
<ul>
<li>SuperPoint Paper (Appendix A) : <a href="https://arxiv.org/pdf/1712.07629.pdf">https://arxiv.org/pdf/1712.07629.pdf</a></li>
<li>Tensorflow Implementation : <a href="https://github.com/rpautrat/SuperPoint">https://github.com/rpautrat/SuperPoint</a> (<code>superpoint/evaluations/detector_evaluation.py</code>).</li>
</ul>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>distance_threshold</code></strong> :&ensp;<code>float</code>, optional</dt>
<dd>Distance from target under which a prediction would be considered valid, by default 2.0</dd>
<dt><strong><code>allow_multimatching</code></strong> :&ensp;<code>bool</code>, optional</dt>
<dd>Allow multiple prediction to match to the same target or not, by default True</dd>
<dt><strong><code>compute_on_step</code></strong> :&ensp;<code>bool</code>, optional</dt>
<dd>Option passed to parent class (c.f. <code>torchmetrics.Metric</code>), by default True</dd>
<dt><strong><code>dist_sync_on_step</code></strong> :&ensp;<code>bool</code>, optional</dt>
<dd>Option passed to parent class (c.f. <code>torchmetrics.Metric</code>), by default False</dd>
<dt><strong><code>process_group</code></strong> :&ensp;<code>Optional[Any]</code>, optional</dt>
<dd>Option passed to parent class (c.f. <code>torchmetrics.Metric</code>), by default None</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class KeypointDetectionAveragePrecision(AveragePrecision):
    &#34;&#34;&#34;Average Precision computed using keypoint position and probabilities.

    This metric implementation has been inspired from :

    * SuperPoint Paper (Appendix A) : https://arxiv.org/pdf/1712.07629.pdf
    * Tensorflow Implementation : https://github.com/rpautrat/SuperPoint (`superpoint/evaluations/detector_evaluation.py`).
    &#34;&#34;&#34;

    def __init__(
        self,
        distance_threshold: float = 2.0,
        allow_multimatching: bool = True,
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
    ) -&gt; None:
        &#34;&#34;&#34;

        Parameters
        ----------
        distance_threshold : float, optional
            Distance from target under which a prediction would be considered valid, by default 2.0
        allow_multimatching : bool, optional
            Allow multiple prediction to match to the same target or not, by default True
        compute_on_step : bool, optional
            Option passed to parent class (c.f. `torchmetrics.Metric`), by default True
        dist_sync_on_step : bool, optional
            Option passed to parent class (c.f. `torchmetrics.Metric`), by default False
        process_group : Optional[Any], optional
            Option passed to parent class (c.f. `torchmetrics.Metric`), by default None
        &#34;&#34;&#34;
        super().__init__(
            num_classes=1,
            compute_on_step=compute_on_step,
            dist_sync_on_step=dist_sync_on_step,
            process_group=process_group,
        )
        self._distance_threshold = distance_threshold
        self._allow_multimatching = allow_multimatching

    def update(
        self, preds: Iterable[torch.Tensor], target: Iterable[torch.Tensor]
    ) -&gt; None:
        &#34;&#34;&#34;Update the metric providing predictions and targets.

        Parameters
        ----------
        preds : Iterable[torch.Tensor]
            Iterable set of predictions. Each prediction should be a N x 3 tensor where `preds[:, 2:]` are the positions and `preds[:, 2]` are the probabilities.
        target : Iterable[torch.Tensor]
            Iterable set of targets. Each target should be a N x 2 tensor containing the targets positions.
        &#34;&#34;&#34;
        ap_target = []
        ap_preds = []

        for p, t in zip(preds, target):
            # skip if no target and no prediction
            if t.numel() == 0 and p.numel() == 0:
                continue
            elif t.numel() == 0:
                # false positives (only predictions, no targets)
                pred_prob = p[:, 2]
                ap_preds.extend(pred_prob.detach().cpu().numpy())
                ap_target.extend([0] * p.shape[0])

            elif p.numel() == 0:
                # false negatives (only targets, no preditions)
                ap_preds.extend([0.0] * t.shape[0])
                ap_target.extend([1] * t.shape[0])
            else:
                # get probability
                pred_prob = p[:, 2]

                # get positions
                pred_posi = p[:, :2].unsqueeze(1)
                targ_posi = t.unsqueeze(0)

                # sort preds in descencing order
                pred_idx = torch.argsort(pred_prob, descending=True)
                pred_prob = pred_prob[pred_idx]
                pred_posi = pred_posi[pred_idx]

                # keep track of unmatched targets
                targ_not_matched = torch.ones(t.shape[0], dtype=bool, device=t.device)

                # compute pairwise distances and potential matches
                delta = pred_posi - targ_posi
                dist = torch.norm(delta, dim=2)
                matches = dist &lt;= self._distance_threshold

                # assign predictions
                for i, m in enumerate(matches):
                    # in non-simplified version, we remove previously matched targets from potential matches
                    if not self._allow_multimatching:
                        m = torch.logical_and(m, targ_not_matched)

                    correct = torch.any(m)
                    if correct:
                        # get closest target among potential matches
                        ap_target_idx = torch.argmin(dist[i] * m)
                        ap_target.append(1)
                        targ_not_matched[ap_target_idx] = False
                    else:
                        ap_target.append(0)
                    ap_preds.append(pred_prob[i])

                # add false negative (unmatched targets)
                for _, not_matched in enumerate(targ_not_matched):
                    if not_matched:
                        ap_preds.append(0.0)
                        ap_target.append(1)

        super().update(
            torch.tensor(ap_preds, device=p.device),
            torch.tensor(ap_target, device=p.device),
        )</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torchmetrics.classification.avg_precision.AveragePrecision</li>
<li>torchmetrics.metric.Metric</li>
<li>torch.nn.modules.module.Module</li>
<li>abc.ABC</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.metrics.keypoint.KeypointDetectionAveragePrecision.preds"><code class="name">var <span class="ident">preds</span> : List[torch.Tensor]</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.metrics.keypoint.KeypointDetectionAveragePrecision.target"><code class="name">var <span class="ident">target</span> : List[torch.Tensor]</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.metrics.keypoint.KeypointDetectionAveragePrecision.update"><code class="name flex">
<span>def <span class="ident">update</span></span>(<span>self, preds: Iterable[torch.Tensor], target: Iterable[torch.Tensor]) ‑> None</span>
</code></dt>
<dd>
<div class="desc"><p>Update the metric providing predictions and targets.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>preds</code></strong> :&ensp;<code>Iterable[torch.Tensor]</code></dt>
<dd>Iterable set of predictions. Each prediction should be a N x 3 tensor where <code>preds[:, 2:]</code> are the positions and <code>preds[:, 2]</code> are the probabilities.</dd>
<dt><strong><code>target</code></strong> :&ensp;<code>Iterable[torch.Tensor]</code></dt>
<dd>Iterable set of targets. Each target should be a N x 2 tensor containing the targets positions.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def update(
    self, preds: Iterable[torch.Tensor], target: Iterable[torch.Tensor]
) -&gt; None:
    &#34;&#34;&#34;Update the metric providing predictions and targets.

    Parameters
    ----------
    preds : Iterable[torch.Tensor]
        Iterable set of predictions. Each prediction should be a N x 3 tensor where `preds[:, 2:]` are the positions and `preds[:, 2]` are the probabilities.
    target : Iterable[torch.Tensor]
        Iterable set of targets. Each target should be a N x 2 tensor containing the targets positions.
    &#34;&#34;&#34;
    ap_target = []
    ap_preds = []

    for p, t in zip(preds, target):
        # skip if no target and no prediction
        if t.numel() == 0 and p.numel() == 0:
            continue
        elif t.numel() == 0:
            # false positives (only predictions, no targets)
            pred_prob = p[:, 2]
            ap_preds.extend(pred_prob.detach().cpu().numpy())
            ap_target.extend([0] * p.shape[0])

        elif p.numel() == 0:
            # false negatives (only targets, no preditions)
            ap_preds.extend([0.0] * t.shape[0])
            ap_target.extend([1] * t.shape[0])
        else:
            # get probability
            pred_prob = p[:, 2]

            # get positions
            pred_posi = p[:, :2].unsqueeze(1)
            targ_posi = t.unsqueeze(0)

            # sort preds in descencing order
            pred_idx = torch.argsort(pred_prob, descending=True)
            pred_prob = pred_prob[pred_idx]
            pred_posi = pred_posi[pred_idx]

            # keep track of unmatched targets
            targ_not_matched = torch.ones(t.shape[0], dtype=bool, device=t.device)

            # compute pairwise distances and potential matches
            delta = pred_posi - targ_posi
            dist = torch.norm(delta, dim=2)
            matches = dist &lt;= self._distance_threshold

            # assign predictions
            for i, m in enumerate(matches):
                # in non-simplified version, we remove previously matched targets from potential matches
                if not self._allow_multimatching:
                    m = torch.logical_and(m, targ_not_matched)

                correct = torch.any(m)
                if correct:
                    # get closest target among potential matches
                    ap_target_idx = torch.argmin(dist[i] * m)
                    ap_target.append(1)
                    targ_not_matched[ap_target_idx] = False
                else:
                    ap_target.append(0)
                ap_preds.append(pred_prob[i])

            # add false negative (unmatched targets)
            for _, not_matched in enumerate(targ_not_matched):
                if not_matched:
                    ap_preds.append(0.0)
                    ap_target.append(1)

    super().update(
        torch.tensor(ap_preds, device=p.device),
        torch.tensor(ap_target, device=p.device),
    )</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.metrics" href="index.html">silk.metrics</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.metrics.keypoint.KeypointDetectionAveragePrecision" href="#silk.metrics.keypoint.KeypointDetectionAveragePrecision">KeypointDetectionAveragePrecision</a></code></h4>
<ul class="">
<li><code><a title="silk.metrics.keypoint.KeypointDetectionAveragePrecision.preds" href="#silk.metrics.keypoint.KeypointDetectionAveragePrecision.preds">preds</a></code></li>
<li><code><a title="silk.metrics.keypoint.KeypointDetectionAveragePrecision.target" href="#silk.metrics.keypoint.KeypointDetectionAveragePrecision.target">target</a></code></li>
<li><code><a title="silk.metrics.keypoint.KeypointDetectionAveragePrecision.update" href="#silk.metrics.keypoint.KeypointDetectionAveragePrecision.update">update</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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